OpenRaiser/Intern-Atlas huggingface dataset:
Solve the Refugee Crisis with Data
Venue: arXiv cs.CY 2024
Year: 2024

In this study, we addressed the refugee crisis through two main models. For predicting the ultimate number of refugees, we first established a Logistic Regression Model, but due to the limited data points, its prediction accuracy was suboptimal. Consequently, we incorporated Gray Theory to develop the Gary Verhulst Model, which provided scientifically sound and reasonable predictions. Statistical tests comparing both models highlighted the superiority of the Gary Verhulst Model. For formulating refugee allocation schemes, we initially used the Factor Analysis Method but found it too subjective and lacking in rigorous validation measures. We then developed a Refugee Allocation Model based on the Analytic Hierarchy Process (AHP), which absorbed the advantages of the former method. This model underwent extensive validation and passed consistency checks, resulting in an effective and scientific refugee allocation scheme. We also compared our model with the current allocation schemes and proposed improvements. Finally, we discussed the advantages and disadvantages of our models, their applicability, and scalability. Sensitivity analysis was conducted, and directions for future model improvements were identified.
Impact of COVID-19 post lockdown on eating habits and lifestyle changes among university students in Bangladesh: a web based cross sectional study
Venue: arXiv cs.CY 2024
Year: 2024

Background:Since the confinement of the lockdown, universities transferred their teaching and learning activities in online as an all-out intention to prevent the transmission of the infection. This study aimed to determine the significant changes in food habits, physical activity, sleeping hours, shopping habits, Internet use time and mental status of the students and investigate the associations between variables. Methods:The study participants were 307 Undergraduate students, between 18 and 25 years of age completed a structured questionnaire from January 3, 2022 to February 13, 2022. The questionnaire included demographic information of the students, questionnaire of dietary pattern, physical activity, sleep quality index, Shopping practice and Internet use time.Chi-square tests were used to associate the baseline information with lifestyle changes in post lockdown. Results:The study reveals that 21.5% of respondents gained weight, 23.8% lost their weight and 41.7% controlled their weight. Eating of homemade food decreased after lockdown 76.5% and eating of restaurant food increased after lockdown 23.5%. A number of major meals 3-4 meals per day decreased after lockdown 61.9%. Physical exercise significantly increased after lockdown (p=0.001). Sleeping hours per day significantly decreased after lockdown (p=0.001), sleep quality was almost the same and energy level increased more in post lockdown. Respondents felt mentally tired after lockdown 60.9%. Respondents spending time on the Internet in chat rooms was 88.3%. Conclusions: This study represents the significant impact on food habits, mental health, and daily routine of students after lockdown, suggesting that we should maintain a balanced diet, physical exercise to sleep quality and mental health.
What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Role-playing games (RPGs) provide players with a rich, interactive world to explore. Dialogue serves as the primary means of communication between developers and players, manifesting in various forms such as guides, NPC interactions, and storytelling. While most games rely on written scripts to define the main story and character personalities, player immersion can be significantly enhanced through casual interactions between characters. With the advent of large language models (LLMs), we introduce a dialogue filler framework that utilizes LLMs enhanced by knowledge graphs to generate dynamic and contextually appropriate character interactions. We test this framework within the environments of Final Fantasy VII Remake and Pokemon, providing qualitative and quantitative evidence that demonstrates GPT-4's capability to act with defined personalities and generate dialogue. However, some flaws remain, such as GPT-4 being overly positive or more subtle personalities, such as maturity, tend to be of lower quality compared to more overt traits like timidity. This study aims to assist developers in crafting more nuanced filler dialogues, thereby enriching player immersion and enhancing the overall RPG experience.
Alignment Scores: Robust Metrics for Multiview Pose Accuracy Evaluation
Venue: arXiv cs.CV 2024
Year: 2024

We propose three novel metrics for evaluating the accuracy of a set of estimated camera poses given the ground truth: Translation Alignment Score (TAS), Rotation Alignment Score (RAS), and Pose Alignment Score (PAS). The TAS evaluates the translation accuracy independently of the rotations, and the RAS evaluates the rotation accuracy independently of the translations. The PAS is the average of the two scores, evaluating the combined accuracy of both translations and rotations. The TAS is computed in four steps: (1) Find the upper quartile of the closest-pair-distances, $d$. (2) Align the estimated trajectory to the ground truth using a robust registration method. (3) Collect all distance errors and obtain the cumulative frequencies for multiple thresholds ranging from $0.01d$ to $d$ with a resolution $0.01d$. (4) Add up these cumulative frequencies and normalize them such that the theoretical maximum is 1. The TAS has practical advantages over the existing metrics in that (1) it is robust to outliers and collinear motion, and (2) there is no need to adjust parameters on different datasets. The RAS is computed in a similar manner to the TAS and is also shown to be more robust against outliers than the existing rotation metrics. We verify our claims through extensive simulations and provide in-depth discussion of the strengths and weaknesses of the proposed metrics.
Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification
Venue: arXiv cs.NE 2024
Year: 2024

Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an event-based optical flow solution based on activation sparsification and a neuromorphic processor, SENECA. SENECA has an event-driven processing mechanism that can exploit the sparsity in ANN activations and SNN spikes to accelerate the inference of both types of neural networks. The ANN and the SNN for comparison have similar low activation/spike density (~5%) thanks to our novel sparsification-aware training. In the hardware-in-loop experiments designed to deduce the average time and energy consumption, the SNN consumes 44.9ms and 927.0 microjoules, which are 62.5% and 75.2% of the ANN's consumption, respectively. We find that SNN's higher efficiency attributes to its lower pixel-wise spike density (43.5% vs. 66.5%) that requires fewer memory access operations for neuron states.
5G NR Positioning with OpenAirInterface: Tools and Methodologies
Venue: arXiv cs.IT 2024
Year: 2024

The fifth-generation new radio (5G NR) technology is expected to provide precise and reliable positioning capabilities along with high data rates. The Third Generation Partnership Project (3GPP) has started introducing positioning techniques from Release-16 based on time, angle, and signal strength using reference signals. However, validating these techniques with experimental prototypes is crucial before successful real-world deployment. This work provides useful tools and implementation details that are required in performing 5G positioning experiments with OpenAirInterface (OAI). As an example use case, we present an round trip time (RTT) estimation test-bed based on OAI and discusses the real-word experiment and measurement process.
A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder
Venue: arXiv cs.CL 2024
Year: 2024

Recently, large language models (LLM) based on transformers are facing memory bottleneck issues due to KV cache, especially in long sequence handling. Previous researches proposed KV cache compression techniques that identify insignificant tokens based on Accumulative Attention Scores and removes their items from KV cache, noting that only few tokens play an important role in attention operations. However, we have observed that the existing Accumulative Attention Score is not suitable for the transformer decoder structure. In the decoder model, the number of times the Attention Score accumulates varies depending on the order of token appearance due to the effect of masking, causing an uneven comparison between tokens. To solve this, we propose Accumulative Attention Score with Forgetting Factor (A2SF) technique, which introduces a Forgetting Factor in the Attention Score accumulation process. A2SF applies a penalty to the past Attention Score generated from old tokens by repeatedly multiplying the Forgetting Factor to the Attention Score over time. Therefore, older tokens receive a larger penalty, providing fairness among different ages of tokens. Through the fair comparison among tokens, we can more effectively select important tokens. We have verified the accuracy improvement through A2SF in the OPT and LLaMA models and A2SF improves the accuracy of LLaMA 2 by up to 7.8% and 5.1% on 1-shot and 0-shot.
MLOPS in a multicloud environment: Typical Network Topology
Venue: arXiv cs.NI 2024
Year: 2024

As artificial intelligence, machine learning, and data science continue to drive the data-centric economy, the challenges of implementing machine learning on a single machine due to extensive data and computational needs have led to the adoption of cloud computing solutions. This research paper explores the design and implementation of a secure, cloud-native machine learning operations (MLOPS) pipeline that supports multi-cloud environments. The primary objective is to create a robust infrastructure that facilitates secure data collection, real-time model inference, and efficient management of the machine learning lifecycle. By leveraging cloud providers' capabilities, the solution aims to streamline the deployment and maintenance of machine learning models, ensuring high availability, scalability, and security. This paper details the network topology, problem description, business and technical requirements, trade-offs, and the provider selection process for achieving an optimal MLOPS environment.
Switchboard: An Open-Source Framework for Modular Simulation of Large Hardware Systems
Venue: arXiv cs.DC 2024
Year: 2024

Scaling up hardware systems has become an important tactic for improving performance as Moore's law fades. Unfortunately, simulations of large hardware systems are often a design bottleneck due to slow throughput and long build times. In this article, we propose a solution targeting designs composed of modular blocks connected by latency-insensitive interfaces. Our approach is to construct the hardware simulation in a similar fashion as the design itself, using a prebuilt simulator for each block and connecting the simulators via fast shared-memory queues at runtime. This improves build time, because simulation scale-up simply involves running more instances of the prebuilt simulators. It also addresses simulation speed, because prebuilt simulators can run in parallel, without fine-grained synchronization or global barriers. We introduce a framework, Switchboard, that implements our approach, and discuss two applications, demonstrating its speed, scalability, and accuracy: (1) a web application where users can run fast simulations of chiplets on an interposer, and (2) a wafer-scale simulation of one million RISC-V cores distributed across thousands of cloud compute cores.
Practical Rely/Guarantee Verification of an Efficient Lock for seL4 on Multicore Architectures
Venue: arXiv cs.LO 2024
Year: 2024

Developers of low-level systems code providing core functionality for operating systems and kernels must address hardware-level features of modern multicore architectures. A particular feature is pipelined "out-of-order execution" of the code as written, the effects of which are typically summarised as a "weak memory model" - a term which includes further complicating factors that may be introduced by compiler optimisations. In many cases, the nondeterminism inherent in weak memory models can be expressed as micro-parallelism, i.e., parallelism within threads and not just between them. Fortunately Jones' rely/guarantee reasoning provides a compositional method for shared-variable concurrency, whether that be in terms of communication between top-level threads or micro-parallelism within threads. In this paper we provide an in-depth verification of the lock algorithm used in the seL4 microkernel, using rely/guarantee to handle both interthread communication as well as micro-parallelism introduced by weak memory models.
Comparison of Large Language Models for Generating Contextually Relevant Questions
Venue: arXiv cs.CL 2024
Year: 2024

This study explores the effectiveness of Large Language Models (LLMs) for Automatic Question Generation in educational settings. Three LLMs are compared in their ability to create questions from university slide text without fine-tuning. Questions were obtained in a two-step pipeline: first, answer phrases were extracted from slides using Llama 2-Chat 13B; then, the three models generated questions for each answer. To analyze whether the questions would be suitable in educational applications for students, a survey was conducted with 46 students who evaluated a total of 246 questions across five metrics: clarity, relevance, difficulty, slide relation, and question-answer alignment. Results indicate that GPT-3.5 and Llama 2-Chat 13B outperform Flan T5 XXL by a small margin, particularly in terms of clarity and question-answer alignment. GPT-3.5 especially excels at tailoring questions to match the input answers. The contribution of this research is the analysis of the capacity of LLMs for Automatic Question Generation in education.
Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we introduce a system based on transfer learning for detecting segment misalignment in multimirror satellites, such as future CubeSat designs and the James Webb Space Telescope (JWST), using image-based methods. When a mirror segment becomes misaligned due to various environmental factors, such as space debris, the images can become distorted with a shifted copy of itself called a "ghost image". To detect whether segments are misaligned, we use pre-trained, large-scale image models trained on the Fast Fourier Transform (FFT) of patches of satellite images in grayscale. Multi-mirror designs can use any arbitrary number of mirrors. For our purposes, the tests were performed on simulated CubeSats with 4, 6, and 8 segments. For system design, we took this into account when we want to know when a satellite has a misaligned segment and how many segments are misaligned. The intensity of the ghost image is directly proportional to the number of segments misaligned. Models trained for intensity classification attempted to classify N-1 segments. Across eight classes, binary models were able to achieve a classification accuracy of 98.75%, and models for intensity classification were able to achieve an accuracy of 98.05%.
Decoding Linguistic Representations of Human Brain
Venue: arXiv cs.CL 2024
Year: 2024

Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the brain. Decoding linguistic representations in the evoked brain has shown groundbreaking achievements, thanks to the rapid improvement of neuroimaging, medical technology, life sciences and artificial intelligence. In this work, we present a taxonomy of brain-to-language decoding of both textual and speech formats. This work integrates two types of research: neuroscience focusing on language understanding and deep learning-based brain decoding. Generating discernible language information from brain activity could not only help those with limited articulation, especially amyotrophic lateral sclerosis (ALS) patients but also open up a new way for the next generation's brain-computer interface (BCI). This article will help brain scientists and deep-learning researchers to gain a bird's eye view of fine-grained language perception, and thus facilitate their further investigation and research of neural process and language decoding.
Configurable Multi-Port Memory Architecture for High-Speed Data Communication
Venue: arXiv cs.AR 2024
Year: 2024

Memory management is necessary with the increasing number of multi-connected AI devices and data bandwidth issues. For this purpose, high-speed multi-port memory is used. The traditional multi-port memory solutions are hard-bounded to a fixed number of ports for read or write operations. In this work, we proposed a pseudo-quad-port memory architecture. Here, ports can be configured (1-port, 2-port, 3-port, 4-port) for all possible combinations of read/write operations for the 6T static random access memory (SRAM) memory array, which improves the speed and reduces the bandwidth for data transfer. The proposed architecture improves the bandwidth of data transfer by 4x. The proposed solution provides 1.3x and 2x area efficiency as compared to dual-port 8T and quad-port 12T SRAM. All the design and performance analyses are done using 65nm CMOS technology.
Fast Static and Dynamic Approximation Algorithms for Geometric Optimization Problems: Piercing, Independent Set, Vertex Cover, and Matching
Venue: arXiv cs.CG 2024
Year: 2024

We develop simple and general techniques to obtain faster (near-linear time) static approximation algorithms, as well as efficient dynamic data structures, for four fundamental geometric optimization problems: minimum piercing set (MPS), maximum independent set (MIS), minimum vertex cover (MVC), and maximum-cardinality matching (MCM). Highlights of our results include the following: * For $n$ axis-aligned boxes in any constant dimension $d$, we give an $O(\log \log n)$-approximation algorithm for MPS that runs in $O(n^{1+δ})$ time for an arbitrarily small constant $δ>0$. This significantly improves the previous $O(\log\log n)$-approximation algorithm by Agarwal, Har-Peled, Raychaudhury, and Sintos (SODA~2024), which ran in $O(n^{d/2}\mathop{\rm polylog} n)$ time. * Furthermore, we show that our algorithm can be made fully dynamic with $O(n^δ)$ amortized update time. Previously, Agarwal et al.~(SODA~2024) obtained dynamic results only in $\mathbb{R}^2$ and achieved only $O(\sqrt{n}\mathop{\rm polylog} n)$ amortized expected update time. * For $n$ axis-aligned rectangles in $\mathbb{R}^2$, we give an $O(1)$-approximation algorithm for MIS that runs in $O(n^{1+δ})$ time. Our result significantly improves the running time of the celebrated algorithm by Mitchell (FOCS~2021) (which was about $O(n^{21})$), and answers one of his open questions. Our algorithm can also be made fully dynamic with $O(n^δ)$ amortized update time. * For $n$ (unweighted or weighted) fat objects in any constant dimension, we give a dynamic $O(1)$-approximation algorithm for MIS with $O(n^δ)$ amortized update time. * For disks in $\mathbb{R}^2$ or hypercubes in any constant dimension, we give the first fully dynamic $(1+\varepsilon)$-approximation algorithms for MVC and MCM with $O(\mathop{\rm polylog}n)$ amortized update time.
3D-GRES: Generalized 3D Referring Expression Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches are limited to segmenting a single target, restricting the versatility of the task. To overcome this limitation, we introduce Generalized 3D Referring Expression Segmentation (3D-GRES), which extends the capability to segment any number of instances based on natural language instructions. In addressing this broader task, we propose the Multi-Query Decoupled Interaction Network (MDIN), designed to break down multi-object segmentation tasks into simpler, individual segmentations. MDIN comprises two fundamental components: Text-driven Sparse Queries (TSQ) and Multi-object Decoupling Optimization (MDO). TSQ generates sparse point cloud features distributed over key targets as the initialization for queries. Meanwhile, MDO is tasked with assigning each target in multi-object scenarios to different queries while maintaining their semantic consistency. To adapt to this new task, we build a new dataset, namely Multi3DRes. Our comprehensive evaluations on this dataset demonstrate substantial enhancements over existing models, thus charting a new path for intricate multi-object 3D scene comprehension. The benchmark and code are available at https://github.com/sosppxo/MDIN.
Steps Towards an Infrastructure for Scholarly Synthesis
Venue: arXiv cs.HC 2024
Year: 2024

Sharing, reusing, and synthesizing knowledge is central to the research process, both individually, and with others. These core functions are not supported by our formal scholarly publishing infrastructure: instead of the smooth functioning of functional infrastructure, researchers resort to laborious "hacks" and workarounds to "mine" publications for what they need, and struggle to efficiently share the resulting information with others. Information scientists have proposed an alternative infrastructure based on the more appropriately granular model of a discourse graph of claims, and evidence, along with key rhetorical relationships between them. However, despite significant technical progress on standards and platforms, the predominant infrastructure remains steadfastly document-based. Drawing from infrastructure studies, we locate the current infrastructural bottlenecks in the lack of local systems that integrate discourse-centric models to augment synthesis work, from which an infrastructure for synthesis can be grown. Through 3 years of research through design and field deployment in a distributed community of hypertext notebook users, we elaborate a design vision of what can and should be built in order to grow a discourse-centric synthesis infrastructure: a thriving "installed base" of researchers authoring local, shareable discourse graphs to improve synthesis work, enhance primary research and research training, and augment collaborative research. We discuss how this design vision -- and our empirical work -- contributes steps towards a new infrastructure for synthesis, and increases HCI's capacity to advance collective intelligence and solve infrastructure-level problems.
CultureVo: The Serious Game of Utilizing Gen AI for Enhancing Cultural Intelligence
Venue: arXiv cs.ET 2024
Year: 2024

CultureVo, Inc. has developed the Integrated Culture Learning Suite (ICLS) to deliver foundational knowledge of world cultures through a combination of interactive lessons and gamified experiences. This paper explores how Generative AI powered by open source Large Langauge Models are utilized within the ICLS to enhance cultural intelligence. The suite employs Generative AI techniques to automate the assessment of learner knowledge, analyze behavioral patterns, and manage interactions with non-player characters using real time learner assessment. Additionally, ICLS provides contextual hint and recommend course content by assessing learner proficiency, while Generative AI facilitates the automated creation and validation of educational content.